Abstract
Three-parameter gamma distribution is extensively used to model skewed data with applications in hydrology, finance and reliability. Parameter estimation in this distribution is rather difficult and procedures based on maximum likelihood and moments are available in the literature. This paper proposes a novel approach based on the principle of least squares, maximum likelihood and maximum product of spacings to estimate the parameters by an extensive search in the three-dimensional space. It overcomes the problem of “seed” specification associated with existing search algorithms. The proposed methodology has been implemented on simulated and real life datasets via a program developed in R 2.15.3. Its performance with regard to estimators based on order statistics are evaluated in terms of the average value, standard deviation and mean square error.
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